Seven Pillars for the Future of Artificial Intelligence

IF 5.6 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Intelligent Systems Pub Date : 2023-12-08 DOI:10.1109/mis.2023.3329745

Erik Cambria, Rui Mao, Melvin Chen, Zhaoxia Wang, Seng-Beng Ho

引用次数: 0

Abstract

In recent years, artificial intelligence (AI) research has showcased tremendous potential to positively impact humanity and society. Although AI frequently outperforms humans in tasks related to classification and pattern recognition, it continues to face challenges when dealing with complex tasks such as intuitive decision making, sense disambiguation, sarcasm detection, and narrative understanding as these require advanced kinds of reasoning, e.g., common-sense reasoning and causal reasoning, which have not been emulated satisfactorily yet. To address these shortcomings, we propose seven pillars that we believe represent the key hallmark features for the future of AI, namely, multidisciplinarity, task decomposition, parallel analogy, symbol grounding, similarity measure, intention awareness, and trustworthiness.

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人工智能未来的七大支柱

近年来，人工智能（AI）研究展现出巨大潜力，对人类和社会产生了积极影响。虽然人工智能在与分类和模式识别相关的任务中经常优于人类，但在处理直觉决策、意义消歧、讽刺检测和叙事理解等复杂任务时，它仍然面临挑战，因为这些任务需要高级推理，如常识推理和因果推理，而这些推理尚未得到令人满意的模拟。为了解决这些不足，我们提出了七大支柱，即多学科性、任务分解、并行类比、符号基础、相似性度量、意图意识和可信性，我们认为这七大支柱代表了未来人工智能的关键标志性特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Intelligent Systems 工程技术-工程：电子与电气

CiteScore

13.80

自引率

3.10%

发文量

122

审稿时长

1 months

期刊介绍： IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications. Typically they are degreed professionals, with backgrounds in engineering, hard science, or business. The publication emphasizes current practice and experience, together with promising new ideas that are likely to be used in the near future. Sample topic areas for feature articles include knowledge-based systems, intelligent software agents, natural-language processing, technologies for knowledge management, machine learning, data mining, adaptive and intelligent robotics, knowledge-intensive processing on the Web, and social issues relevant to intelligent systems. Also encouraged are application features, covering practice at one or more companies or laboratories; full-length product stories (which require refereeing by at least three reviewers); tutorials; surveys; and case studies. Often issues are theme-based and collect articles around a contemporary topic under the auspices of a Guest Editor working with the EIC.